Amazon SageMaker Archives - Page 2 of 21

Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

In this post, we demonstrate how you can address the challenges of model customization being complex, time-consuming, and often expensive by using fully managed environment with Amazon SageMaker Training jobs…

Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

In this post, we show you how to implement an audio and video segmentation solution using SageMaker Ground Truth. We guide you through deploying the necessary infrastructure using AWS CloudFormation,…

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards, making it straightforward to manage governance information for specific model versions directly…

Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the…

Train, optimize, and deploy models on edge devices using Amazon SageMaker and Qualcomm AI Hub

In this post we introduce an innovative solution for end-to-end model customization and deployment at the edge using Amazon SageMaker and Qualcomm AI Hub.

Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text…

Efficient Pre-training of Llama 3-like model architectures using torchtitan on Amazon SageMaker

In this post, we collaborate with the team working on PyTorch at Meta to showcase how the torchtitan library accelerates and simplifies the pre-training of Meta Llama 3-like model architectures.…

How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMaker

In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the…

Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker

In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use Meta’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by…

Build ultra-low latency multimodal generative AI applications using sticky session routing in Amazon SageMaker

In this post, we explained how the new sticky routing feature in Amazon SageMaker allows you to achieve ultra-low latency and enhance your end-user experience when serving multi-modal models.